Heterogeneous Computing Performance Analysis Suite

Project Vision

Modern computing workloads demand intelligent selection between CPU, GPU, and distributed execution strategies. This project implements the same computational kernel—matrix multiplication—across three different architectures, then rigorously analyzes their performance characteristics. The goal is to demonstrate not just how to implement these approaches, but when each architecture provides optimal performance.

System Design

Three-Tiered Implementation

1. CPU Baseline (AMD Ryzen 9 5950X)

Naive Implementation: Standard triple-nested loop serving as reference
AVX2 SIMD Optimization: Vectorized operations processing 8 floats simultaneously
Cache Optimization: Loop tiling and data layout for L1/L2/L3 efficiency
Multi-threading: OpenMP parallelization across 16 cores/32 threads

2. GPU Acceleration (NVIDIA RTX 4070)

Naive CUDA Kernel: One thread per output element baseline
Shared Memory Tiling: Reducing global memory bandwidth bottleneck
Warp-Level Optimization: Coalesced memory access patterns
cuBLAS Integration: Comparison against highly-optimized vendor library

3. Distributed Multi-GPU System

gRPC Service Layer: Remote procedure calls for GPU task distribution
Work-Stealing Scheduler: Dynamic load balancing across heterogeneous GPUs (RTX 4070 + RTX 2060)
Network Protocol Optimization: Minimizing serialization and transfer overhead
Fault Tolerance: Handling worker disconnection and task reassignment

Performance Analysis Framework

This project goes beyond simple “faster vs slower” comparisons by implementing scientific performance modeling:

Roofline Analysis

The roofline model visualizes the theoretical performance ceiling based on two fundamental limits:

Compute Bound: Peak floating-point operations per second (FLOPS)
Memory Bound: Peak memory bandwidth (GB/s)

For each implementation, the project will:

Calculate operational intensity (FLOP/byte ratio)
Measure achieved performance (GFLOPS)
Plot against theoretical roofline to identify bottlenecks
Determine optimization headroom

Nsight Compute Profiling

Using NVIDIA’s profiling tools to extract detailed GPU metrics:

Memory throughput and bandwidth utilization
SM (Streaming Multiprocessor) occupancy
Warp execution efficiency
Instruction-level bottlenecks

Expected Performance Characteristics

CPU SIMD: 30-50% of peak theoretical performance (memory-bound)
GPU Single: 70-90% of peak bandwidth (well-optimized tiling)
Multi-GPU: 1.4-1.6x single GPU (limited by network overhead)

Technical Challenges

Challenge 1: SIMD Alignment and Intrinsics

AVX2 requires 32-byte aligned memory and explicit intrinsic functions. Managing pointer alignment, handling non-divisible dimensions, and debugging vector operations adds significant complexity compared to naive loops.

Challenge 2: CUDA Memory Hierarchy

GPU performance is highly sensitive to memory access patterns. Achieving 80%+ of peak bandwidth requires:

Tile sizes matching shared memory capacity
Coalesced global memory accesses (threads in a warp access contiguous addresses)
Register pressure management to maintain high occupancy

Challenge 3: Distributed Load Balancing

The RTX 4070 is approximately 2.5x faster than the RTX 2060. Static work partitioning leads to the slow GPU becoming the bottleneck. The work-stealing approach allows the faster GPU to claim additional tasks dynamically, but requires careful synchronization and minimal locking overhead.

Benchmarking Methodology

To ensure scientific rigor, all measurements will follow these principles:

Multiple Runs: Each configuration tested 10+ times, reporting median and variance
Warm-up Period: Discarding initial runs to eliminate cold-start effects
Controlled Environment: Monitoring CPU/GPU temperatures, throttling, and background processes
Scaling Analysis: Testing matrix sizes from 512×512 to 4096×4096
Power Measurement: Comparing performance-per-watt across architectures

Expected Deliverables

Quantitative Results

Performance comparison table (time, GFLOPS, speedup)
Roofline plots for each architecture
Scaling behavior graphs (strong/weak scaling)
Power efficiency analysis

Technical Documentation

Detailed explanation of each optimization technique
Code annotations explaining critical sections
Decision tree for architecture selection based on workload characteristics

Visualization

Interactive Grafana dashboard showing real-time performance
Publication-quality matplotlib figures for technical reports
Architecture diagrams illustrating data flow

Real-World Applications

Understanding heterogeneous computing performance is critical for:

Scientific Computing: HPC clusters with mixed CPU/GPU nodes
Cloud Services: AWS EC2 instances with varying accelerator types
Machine Learning: Training across multiple GPUs with PyTorch/TensorFlow
Graphics Rendering: Distributed ray tracing farms
Financial Modeling: Monte Carlo simulations on GPU clusters

Why This Project Stands Out

Most undergraduates implement either CPU or GPU code, rarely both, and almost never with rigorous performance analysis. This project demonstrates:

Systems Thinking: Understanding performance at the architecture level
Scientific Methodology: Proper benchmarking and analysis techniques
Production Skills: Distributed systems, RPC protocols, performance monitoring
Communication: Translating complex results into clear visualizations

These skills directly translate to roles in high-performance computing, systems engineering, and infrastructure optimization at companies building cloud platforms, ML infrastructure, or scientific computing tools.

Future Enhancements

Additional Kernels: FFT, convolution, sorting for diverse workload analysis
FPGA Integration: Adding Zynq FPGA as fourth compute node
Auto-Tuning: Dynamically selecting optimal tile sizes and work distribution
Energy Modeling: Extending roofline analysis to performance-per-watt